Staff Profile:Dr Suvarna Punalekar

Name:
Dr Suvarna Punalekar
Job Title:
Post-Doctoral Research Assistant
Responsibilities:
  • Post-Doctoral Research Assistant on Innovate-UK funded PASQUAL project
Areas of Interest:
  • Ecology, ecosystem modelling, remote sensing, vegetation spectroscopy
  • Development and applications of soil vegetation atmosphere transfer (SVAT) models
  • Application of multi and hyperspectral remote sensing data for vegetation biophysical and biochemical characterization
  • Development of combined application of remote sensing and ecosystem models to monitor vegetation dynamics
Research groups / Centres:

Environmental Science Research Division

Project Information

The project aim is the development of pasture quality and quantity monitoring tool for dairy farmers. This tool will combine potentials of remote sensing data and vegetation growth models in order to track pasture growth and further to predict near future changes. My responsibilities are mainly development, calibration and verification of pasture models and associated field data collection. I am mainly responsible to design and execute field spectral and vegetation biophysical-chemical data collection. Using these data I develop two different vegetation parameter retrieval strategies – first simple and widely used vegetation index based empirical method and second process based radiative transfer inversion method. The parameters of interest are Leaf Area Index, Leaf biochemical contents, Leaf inclination. Pasture quality indicators such as fibre content, leaf nitrogen content would be studied mainly using empirical approach. Further, my work involves development of a framework for assimilating these remotely sensed parameters in the growth model calibration and development.

Key Facts

I have followed my research interest in vegetation ecology throughout my academic and professional career. I did B.Sc. in Botany from Mumbai University, India in 2006 and M.Sc. in Environmental Science from Pune University, India, in 2008. During my M.Sc., I became curious about application of remote sensing for vegetation monitoring. Following this interest I did Post-Graduate Diploma in Remote Sensing and GIS from Centre for Development of Advanced Computing, Pune. I then joined Indian Space Research Organisation (ISRO), Ahmedabad, India, as a Research Fellow and worked on monitoring phenological changes in semi-evergreen and deciduous forests of central India using satellite products. After working in ISRO for nearly a year and half and getting quite a bit of professional experience in remote sensing I started doctoral research in the Department of Geography and Environmental Science, University of Reading.

The overarching objective of the doctoral thesis was to assess the potential and limitations of modern techniques such as hyperspectral remote sensing and complex SVAT models for monitoring and modelling floodplain meadow ecosystems. A complex semi-mechanistic coupled radiative transfer-energy balance-photosynthesis model called SCOPE was used for this research.

The spectral variability of key vegetation species was characterized using field as well as aerial hyperspectral data. The study found that monitoring a modest number of key target species using hyperspectral data and an ‘optical-functional trait’ based approach can be an effective tool to monitor changes in these biodiverse communities. The spectral data were then tested for retrieval of important biophysical parameters (including Leaf Area Index) by inverting a canopy radiative transfer model. The retrieved parameters were used as input to the SCOPE model for the simulation of evapotranspiration and energy fluxes from the meadow vegetation. The combined use of hyperspectral data and the SCOPE model was found to simulate the diurnal photosynthesis and energy fluxes effectively.

Through my doctoral research I gained specific research-level expertise in working with complex SVAT models, large field and remotely sensed datasets, and field data sampling. Physically based ecosystem models are powerful tools to understand complex interactions between vegetation and its environment. In my career I would like to work on improving model representation of different biotic and abiotic processes, improving spatiotemporal representation of model parameters and processes for enhancing the predictive power of these models with reference to changes in the environment and resource management. Furthermore I am keen to work on research projects that can be directly beneficial to the community and would demonstrate actual application of high end technological advancement for the better resource management and better environment.

You can see more about my research profile at Suvarna Punalekar

Publications:

S.M. Punalekar, A. Verhoef, I. V. Tatarenko, C. van der Tol, D.M.J. Macdonald, B. Marchant, F. Gerard, D. Gowing, K. White, "Characterization of highly biodiverse floodplain meadows using hyperspectral remote sensing within a plant functional trait framework", Remote Sensing, 2016, 8(2), 112.

S. Punalekar, P.K. Gupta, M.P. Oza, R.P. Singh, A. Sonakia, "Estimation of phenological parameters using remote sensing derived high temporal LAI data: A case study of forest region in central India", Journal of Geomatics, 2015, 9(1), 77-85.

J.F. Calleja, C. Hellmann, G. Mendiguren, S. Punalekar, J. Peon, A. MacArthur, L. Alonso, "Relating hyperspectral airborne data to ground measurements in a complex and discontinuous canopy", Acta Geophysica, 2015, ISSN (Online) 1895-7455. DOI: 10.1515/acgeo-2015-0036.

P.K. Gupta, S. Punalekar, S. Panigrahy, A. Sonakia, J.S. Parihar, "Run-off modelling in an agro-forested watershed using Remote sensing and GIS", ASCE Journal of Hydrologic Engineering, 2012, 17(11), 1255-1267.

S. Punalekar, D. Mahajan, D. Kulakrni, "Impact of exotic tree plantation on the native tree species of Vetal hill, Pune", Indian Journal of Forestry, 2010, 33(4), 549-554.

PhD Thesis

Punalekar, S., 2015. Synergistic use of hyperspectral remote sensing data and SVAT modelling to advance ecosystem research. University of Reading

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